a6.R

#Install the pakages if you haven't already done so
install.packages('nnet')

nnet = neuralnet(Occupancy ~ Temperature + Humidity + Light + CO2 + HumidityRatio,data=train,linear.output = FALSE,hidden = c(3,2))

#Predicted values
pred = compute(nnet,test[,-6])
pred = pred$net.result

#From the graph, for all predictions greater than 0.7 1 else 0
pred[pred>=0.7] = 1
pred[pred<0.7] = 0
pred = factor(pred)
plot(pred)

print(round(perf(pred,test$Occupancy),5)* 100)

#Output		         :	0.9556757727
#Execution Time 	 :	1.6 minutes